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--- Diff: core/src/main/scala/org/apache/spark/executor/Executor.scala ---
@@ -136,6 +136,26 @@ private[spark] class Executor(
// for fetching remote
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--- Diff: core/src/main/scala/org/apache/spark/storage/RDDInfo.scala ---
@@ -53,10 +55,16 @@ class RDDInfo(
}
private[spark] object
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@@ -72,6 +72,9 @@ package object config {
private[spark] val
Github user tigerquoll commented on the issue:
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@rdblue when you say "you don't think the API proposed here needs to
support a first-class partition concept", are you referring to the
"DeleteSupport" Interface, or to DataSourceV2 in general?
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---
@@ -1390,7 +1395,11 @@ class
Github user cloud-fan commented on the issue:
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Can you define the scope of this PR? In which case we should change the
references in the join condition?
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Github user jerryshao commented on the issue:
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Jackson version below 2.9.5 has CVE issues, I would suggest to upgrade to
2.9.6 as #21596 did.
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Github user asfgit closed the pull request at:
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--- Diff: R/pkg/R/catalog.R ---
@@ -69,7 +69,6 @@ createExternalTable <- function(x, ...) {
#' @param ... additional named parameters as options
Github user MichaelChirico commented on a diff in the pull request:
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--- Diff: R/pkg/R/catalog.R ---
@@ -69,7 +69,6 @@ createExternalTable <- function(x, ...) {
#' @param ... additional named parameters as options
Github user mgaido91 commented on a diff in the pull request:
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--- Diff:
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@@ -293,6 +293,28 @@ class AstBuilder(conf: SQLConf)
Github user mgaido91 commented on the issue:
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@HyukjinKwon I am sure, since I tried removing the added check and the UT I
added here passed.
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--- Diff: R/pkg/R/functions.R ---
@@ -3404,19 +3404,24 @@ setMethod("collect_set",
#' Equivalent to \code{split} SQL function.
#'
#'
Github user LantaoJin commented on the issue:
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Thanks @dongjoon-hyun . That would be a problem. Seems setting to 200 or
500 are cause a limited regression on hover text.
Hard code to 500 shows:
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Can one of the admins verify this patch?
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Github user jerryshao commented on the issue:
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Do we officially support hadoop3 in branch 2.4? If branch 2.4 doesn't
target to support Hadoop3 and this fix is only for Hadoop3, then I don't think
it is meaningful to have this fix.
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Github user cloud-fan commented on the issue:
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> BTW, I believe there's no particular standard for backticks themselves
since different DBMS uses different backtick implementations.
You are right, but SQL standard does define how to
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How much perf can we save here? I don't think shuffle writing will be
bottlenecked by this lock.
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Github user cloud-fan commented on the issue:
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retest this please
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--- Diff: python/pyspark/sql/dataframe.py ---
@@ -880,18 +880,23 @@ def sampleBy(self, col, fractions, seed=None):
| 0|5|
Github user kiszk commented on a diff in the pull request:
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--- Diff:
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@@ -223,9 +223,9 @@ trait
Github user NiharS commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/util/Utils.scala ---
@@ -240,6 +240,19 @@ private[spark] object Utils extends Logging {
//
Github user gatorsmile commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/storage/RDDInfo.scala ---
@@ -53,10 +55,16 @@ class RDDInfo(
}
private[spark] object
Github user HyukjinKwon commented on the issue:
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I mean
https://spark.apache.org/docs/latest/sql-programming-guide.html#supported-hive-features
and
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retest this please
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@@ -69,12 +69,25 @@ class
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--- Diff: R/pkg/R/catalog.R ---
@@ -69,7 +69,6 @@ createExternalTable <- function(x, ...) {
#' @param ... additional named parameters as options
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retest this please
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Github user mgaido91 commented on the issue:
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kindly ping @cloud-fan
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@@ -646,7 +647,47 @@ private[spark] class AppStatusListener(
}
Github user jerryshao commented on the issue:
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Btw, I don't think we can run current Spark with Hadoop 3.1 without any
change.
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We do not need to follow Hive if Hive does not follow SQL compliance. Our
main goal is to follow the mainstream DBMS vendors.
BTW, we can enhance our parser to recognize the other
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The purpose is logging meta info like file input file path to event log. So
I revert the changes about simpleString and add back the metadata to
SparkPlanInfo interface. This change will log
Github user wangyum commented on a diff in the pull request:
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--- Diff: pom.xml ---
@@ -2694,6 +2694,8 @@
3.1.0
2.12.0
3.4.9
+2.7.8
+
2.7.8
--- End
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--- Diff: pom.xml ---
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3.1.0
2.12.0
3.4.9
+2.7.8
+
2.7.8
--- End
Github user seancxmao commented on the issue:
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@cloud-fan @gatorsmile I think the old `Upgrading From Spark SQL 2.3.1 to
2.3.2 and above` is not needed since we do not backport SPARK-25132 to
branch-2.3. I'm wondering if we need `Upgrading
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--- Diff: R/pkg/R/catalog.R ---
@@ -69,7 +69,6 @@ createExternalTable <- function(x, ...) {
#' @param ... additional named parameters as options
Github user HyukjinKwon commented on the issue:
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Seems fine to me.
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Github user rvesse commented on the issue:
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@vanzin I think in the current implementation of this PR the Kerberos login
is happening inside the driver pod which is running inside the K8S cluster.
The old design from the Spark on K8S
Github user wangyum commented on the issue:
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I did a simple test for 2.9.6. It works well. But that pr for 3.0. It means
that a simple test on branch 2.4 will fail:
```scala
scala> spark.range(10).write.parquet("/tmp/spark/parquet")
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Yea, I didn't mean it super seriously @cloud-fan - I just left a comment in
case for a better documentation since I see many users go from Hive to Spark.
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Github user HyukjinKwon commented on the issue:
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@mgaido91, BTW are you sure SPARK-21281 introduced that behaviour change?
Before:
```
scala> import org.apache.spark.sql.functions.struct
import
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Thanks @dbtsai and @HyukjinKwon. Your comments are addressed.
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GitHub user MaxGekk opened a pull request:
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[SPARK-25393][SQL] Adding new function from_csv()
## What changes were proposed in this pull request?
The PR adds new function `from_csv()` similar to `from_json()` to parse
columns with
Github user viirya commented on a diff in the pull request:
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Github user peter-toth commented on the issue:
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@cloud-fan this PR doesn't solve that question.
There are some hacks in `Dataset.join` to handle `EqualTo` and
`EqualNullSafe` with duplicated attributes and those hacks are still required
Github user cloud-fan commented on the issue:
https://github.com/apache/spark/pull/22010
I think this works, can we post some Spark web UI screenshots to confirm
the shuffle is indeed eliminated?
BTW one idea to simplify the implementation:
```
def
Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/21433#discussion_r216213289
--- Diff: core/src/main/scala/org/apache/spark/storage/RDDInfo.scala ---
@@ -53,10 +55,16 @@ class RDDInfo(
}
private[spark] object
Github user cloud-fan commented on the issue:
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> Spark SQL is designed to be compatible with the Hive Metastore, SerDes
and UDFs.
This is different from `Spark can run any Hive SQL`. Spark can load and use
Hive UDFs, with the right
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--- Diff: python/pyspark/sql/dataframe.py ---
@@ -880,18 +880,23 @@ def sampleBy(self, col, fractions, seed=None):
| 0|5|
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@@ -199,6 +209,15 @@ private[sql]
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@@ -72,6 +72,9 @@ package object config {
private[spark] val
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